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Boehringer Ingelheim (Germany)

Germany

21 papers here1.6k citations across 21
fields
  • quant-ph9
  • stat.AP4
  • cs.LG2
  • eess.SY2
  • cs.CV1
  • q-bio.OT1
  • q-bio.TO1
  • stat.ME1
ROR 00q32j219OpenAlex

affiliations via OpenAlex

output
20132025
most citedCommon pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

1k citations

researchers with a paper here
  • M. Degroote9 · h 18
  • Michael Streif8 · h 16
  • C. Tautermann5 · h 35
  • Nikolaj Moll5 · h 7
  • G. Anselmetti4 · h 12
  • Raffaele Santagati4 · h 10
  • R. Santagati4 · h 24
  • K. Rufibach3 · h 41
  • N. Moll3 · h 35
  • N. Rubin3 · h 48
  • N. Wiebe3 · h 60
  • R. Babbush3 · h 73
collaborating institutions
  • Roche (Switzerland)CH5 papers
  • Novartis (Switzerland)CH4 papers
  • Science Wares (United States)US4 papers
  • University of TorontoCA4 papers
  • Bayer (Germany)DE3 papers
  • Google (United States)US3 papers
  • Imperial College LondonGB3 papers
  • Pacific Northwest National LaboratoryUS3 papers
  • AstraZeneca (United Kingdom)GB2 papers
  • Boehringer Ingelheim (China)CN2 papers
  • Boehringer Ingelheim (Egypt)EG2 papers
  • Bristol-Myers Squibb (Germany)DE2 papers
Showing cs.CVShow all

1 paper · 1 filter

cs.CV2022★ 25 cited

Learning image representations for anomaly detection: application to discovery of histological alterations in drug development

Igor Zingman, Birgit Stierstorfer, Charlotte Lempp +1

We present a system for anomaly detection in histopathological images. In histology, normal samples are usually abundant, whereas anomalous (pathological) cases are scarce or not a…

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